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@Syderitic
Syderitic / Secure_Encrypted_Storage_Setup.md
Last active June 16, 2026 16:45
Secure Encrypted Storage Setup with LUKS2, TPM2, FIDO2, and Btrfs

Secure Encrypted Storage Setup with LUKS2, TPM2, FIDO2, and Btrfs

Disclaimer: Use this guide at your own discretion. The author is not responsible for any data loss or security breaches that may occur. Always ensure you understand each step and its implications before proceeding.

This guide walks you through setting up a secure external drive, perfect for backing up sensitive data, securely handing over large data sets, or storing important files. It covers creating an encrypted drive that’s easy to unlock with trusted devices, such as a security key, while remaining highly secure if misplaced. Ideal for safeguarding personal data or business files, it offers practical recovery steps to ensure access even if you change devices.

By following this guide, you will have established a secure and user-friendly encrypted storage solution that leverages:

  • LUKS2 Encryption: Provides robust disk encryption.
  • TPM2 Integration: Enables automatic unlocking on trusted hardware.
  • FIDO2 Tokens: Offers flexible acc

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

@k16shikano
k16shikano / SKILL.md
Last active June 16, 2026 16:41
japanese-tech-writing/SKILL
name japanese-tech-writing
description 日本語の技術文書・書籍原稿の文章規範。整形(一文一行、引用ブロック、脚注、コラム記法)、段落と論証の構成(パラグラフライティング)、論証の厳密さ(ツッコミどころの除去)、読み手の負荷の管理、視点と語り、演出の抑制、LLM っぽい空句の禁止、冗長の排除を定める。日本語で技術書の章、草稿、記事、解説文を書くとき、または推敲・リライトするときに使用する。

日本語技術文書の文章規範

日本語で技術的な原稿(書籍の章、記事、解説文)を書く・推敲するときは、以下の規範に従う。

整形

@Turkidev
Turkidev / huawei-ax3-dns-interceptor.user.js
Last active June 16, 2026 16:41
Huawei AX3 Router – DNS override interceptor (fix hidden DNS fields in web UI)
/**
* Huawei AX3 DNS Interceptor (hardened)
* - Catches Request objects and string URLs
* - Matches by pathname to survive host changes
* - Safely clones & rewrites JSON bodies
*
* Edit these:
*/
const PAGE_IP = location.hostname || "192.168.100.1"; // your router IP (auto-uses current host)
const CUSTOM_DNS1 = "1.1.1.1"; // primary DNS
@vitiko98
vitiko98 / get_keys.py
Last active June 16, 2026 16:34
Get Qobuz App ID and Secrets
from qobuz_dl.bundle import Bundle
bundle = Bundle()
app_id = bundle.get_app_id()
secrets = "\n".join(bundle.get_secrets().values())
print(f"App ID: {app_id}")
print("#" * 20)
"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp